Connectome-based predictive modeling of empathy in adolescents with and without the low-prosocial emotion specifier

被引:0
|
作者
Winters, Drew E. [1 ]
Guha, Anika [1 ]
Sakai, Joseph T. [1 ]
机构
[1] Univ Colorado, Dept Psychiat, Sch Med, Anschutz Med Campus, Aurora, CO 80045 USA
关键词
Connectome-based predictive modeling; Functional connectivity; Empathy; Callous -unemotional traits; Adolescents; CALLOUS-UNEMOTIONAL TRAITS; BRAIN; BEHAVIOR; SYSTEM;
D O I
10.1016/j.neulet.2023.137371
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Empathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n = 29) and those that do not (n = 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps.
引用
收藏
页数:8
相关论文
共 45 条
  • [1] Connectome-based predictive modeling of trait forgiveness
    Li, Jingyu
    Qiu, Jiang
    Li, Haijiang
    SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE, 2023, 18 (01)
  • [2] Connectome-Based Predictive Modeling of Trait Mindfulness
    Treves, Isaac N.
    Kucyi, Aaron
    Park, Madelynn
    Kral, Tammi R. A.
    Goldberg, Simon B.
    Davidson, Richard J.
    Rosenkranz, Melissa
    Whitfield-Gabrieli, Susan
    Gabrieli, John D. E.
    HUMAN BRAIN MAPPING, 2025, 46 (01)
  • [3] Connectome-Based Predictive Modeling of Individual Anxiety
    Wang, Zhihao
    Goerlich, Katharina S.
    Ai, Hui
    Aleman, Andre
    Luo, Yue-Jia
    Xu, Pengfei
    CEREBRAL CORTEX, 2021, 31 (06) : 3006 - 3020
  • [4] Connectome-Based Predictive Modeling of Creativity Anxiety
    Ren, Zhiting
    Daker, Richard J.
    Shi, Liang
    Sun, Jiangzhou
    Beaty, Roger E.
    Wu, Xinran
    Chen, Qunlin
    Yang, Wenjing
    Lyons, Ian M.
    Green, Adam E.
    Qiu, Jiang
    NEUROIMAGE, 2021, 225
  • [5] Functional Connectome-Based Predictive Modeling in Autism
    Horien, Corey
    Floris, Dorothea L.
    Greene, Abigail S.
    Noble, Stephanie
    Rolison, Max
    Tejavibulya, Link
    O'Connor, David
    McPartland, James C.
    Scheinost, Dustin
    Chawarska, Katarzyna
    Lake, Evelyn M. R.
    Constable, R. Todd
    BIOLOGICAL PSYCHIATRY, 2022, 92 (08) : 626 - 642
  • [6] Connectome-based predictive modeling of Internet addiction symptomatology
    Feng, Qiuyang
    Ren, Zhiting
    Wei, Dongtao
    Liu, Cheng
    Wang, Xueyang
    Li, Xianrui
    Tie, Bijie
    Tang, Shuang
    Qiu, Jiang
    SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE, 2024, 19 (01)
  • [7] Neural mechanisms underlying empathy during alcohol abstinence: evidence from connectome-based predictive modeling
    Guanzhong Yao
    Luqing Wei
    Ting Jiang
    Hui Dong
    Chris Baeken
    Guo-Rong Wu
    Brain Imaging and Behavior, 2022, 16 : 2477 - 2486
  • [8] Neural mechanisms underlying empathy during alcohol abstinence: evidence from connectome-based predictive modeling
    Yao, Guanzhong
    Wei, Luqing
    Jiang, Ting
    Dong, Hui
    Baeken, Chris
    Wu, Guo-Rong
    BRAIN IMAGING AND BEHAVIOR, 2022, 16 (06) : 2477 - 2486
  • [9] Connectome-based predictive modeling of early and chronic psychosis symptoms
    Foster, Maya L.
    Ye, Jean
    Powers, Albert R.
    Dvornek, Nicha C.
    Scheinost, Dustin
    NEUROPSYCHOPHARMACOLOGY, 2025,
  • [10] Connectome-based predictive modeling for functional recovery of acute ischemic stroke
    Peng, Syu-Jyun
    Chen, Yu-Wei
    Hung, Andrew
    Wang, Kuo-Wei
    Tsai, Jang-Zern
    NEUROIMAGE-CLINICAL, 2023, 38